@Studentmaniac
I watched half way now I will try to complete with the pace of MIT Schedules
@vorsybl
These courses released for free by MIT OCW have been invaluable to me. Thanks again. The content is nothing short of gold.
@TheAnimeist
This is a solid course. Being an experienced programmer I still watched the entire lecture. It's a strong base to start from.
@tmo314
Chapters: 00:00 - Intro 05:05 - Data and Algorithms 10:53 - Memory Storage 15:15 - Language 21:40 - Data Types 31:00 - Expressions 40:38 - Variables 55:33 - Python Tutor 1:01:40 - Summary
@sergemoktali7948
From Africa thanks a lot. There's no way we might have such a qualitative (and it's free, may God bless you) content from our universities out here. This might make a big difference ❤
@pressure8902
Thank you MIT and the professor sharing this for free for all of students around the world, who can't afford MIT education. This sure makes meaningful impact on a lot of us
@jellybean3078
I know most of y'all are starting from here it's gonna be 2025 but you're not late you're at the right time best of luck 🤞🏻
@thetkhaung1612
I started with Havard CS 50X and simultaneously taking Havard Introduction to programming with python and then i fortunately found this course and thanks , now , i am taking the later two courses simultaneously. Both of Prof. David Malan and Prof. Ana Bell are becoming my favourite teachers .
@Amingo21
Computer science student from Africa taking this course is really helpful.
@danielfox3399
39:26 - Python gives floats in divisions to ensure precise results, even with fractional values. For integer results, use floor division (//).
@surajshukla-r7u
really loved how professor is breaking it down bit by bit to understand very subtle nuances like introducing third variable in the last example. absolutely gold stuff by mit
@josephcote6120
I'm retired from a long career as a programmer and programming teacher. Learned and used a lot of languages along the way, but never happened to do Python. Enjoying this series to see what I might have been missing and how Python differs from what I already know.
@anthonyrhopkins
00:00-00:16 “Introduction to the Course” The lecture begins with a welcome message from Ana Bell, who introduces herself and the course, 6.100L. She mentions her experience in the EECS Department and expresses enthusiasm for teaching the full semester version of the subject. 00:16-00:39 “Course Overview” Ana Bell outlines the course structure, which includes both administrative details and content focused on computer functionality and Python programming. She emphasizes the hands-on nature of the course by encouraging students to code along during the session. 00:39-01:58 “Interactive Learning Approach” The instructor explains the importance of participation and practice in learning programming. She introduces the "you try it" breaks, where students can practice coding skills in real-time, and reassures them that they will collaborate on unfinished programs together as a class. 01:58-03:06 “Key Learning Objectives” Ana highlights the main outcomes of the course, which include gaining programming skills, understanding computer science concepts, and developing problem-solving abilities. She encourages students to bridge the gap between understanding a problem and applying computer science concepts through programming. 03:06-04:51 “Concepts and Topics Covered” The focus is on computational thinking, where students learn to approach problems with a computational mindset rather than a purely mathematical one. The course will guide students in learning Python, organizing code, and exploring basic algorithms while considering algorithmic complexity towards the end. 04:51-06:09 “Declarative vs. Imperative Knowledge” The lecture transitions to discussing types of knowledge in computer science, contrasting declarative and imperative knowledge. Ana explains that programming involves creating instructions or "recipes," emphasizing that computers require clear, actionable steps to perform tasks. 06:09-07:55 “Example of Finding Square Roots” Ana presents a practical example by demonstrating how to compute the square root of a number using an iterative method. She details the steps involved in refining guesses until arriving at an acceptable approximation, showcasing how programming can translate mathematical concepts into computational processes. 07:55-08:32 “Understanding Algorithms” Ana introduces the concept of algorithms, describing them as structured sequences of steps that direct the flow of control in problem-solving processes. She emphasizes the importance of having clear stopping points in algorithms to prevent them from running indefinitely, demonstrating this with her previous example regarding the square root approximation. 08:32-09:06 “Recipes as Algorithms” Drawing a parallel between algorithms and cooking, Ana explains how recipes function similarly by providing a sequence of steps to achieve an end result. She uses the example of baking a cake, discussing various decision points within a recipe that guide the user, and highlighting how both rely on clear instructions to lead to the successful completion of a task. 09:06-09:54 “Nature of Computers” Ana discusses the fundamental nature of computers, characterizing them as tools that execute algorithms without independent thought or decision-making capability. She points out their strengths, such as rapid data processing and extensive data storage, but reiterates that they strictly follow the instructions provided by the programmer. 09:54-10:12 “Key Takeaway on Programming” Ana stresses a vital takeaway for students: computers will only perform tasks they are explicitly instructed to do. She accentuates the importance of understanding this concept in the context of programming, ensuring that students grasp the necessity of detailing every step in their coding projects. 10:12-11:36 “Historical Context of Programming” Transitioning to a brief history of programming, Ana contrasts fixed-program computers before the 1940s with stored-program computers that emerged afterward. She explains how earlier devices required manual input of operations each time, whereas later computers could store instructions, enabling them to execute sequences of tasks automatically through a program interpreter. 11:36-12:03 “Low-Level Computer Operations” Ana provides a simple overview of how computers operate at a low level, discussing key components such as memory storage, the arithmetic logic unit responsible for performing mathematical operations, and the control unit which manages the execution of instructions. This foundational knowledge serves as an introduction to understanding how Python and higher-level programming languages interact with hardware. 12:03-12:59 “Executing a Simple Program” Ana illustrates the execution of a simple program, describing how the interpreter processes instructions using memory locations. She explains how the arithmetic logic unit (ALU) adds values stored in specific memory locations, such as adding 3 and 4, and stores the result in another memory location—a process she emphasizes as tedious yet fundamental to understanding programming. 12:59-14:04 “Deep Dive into Comparisons and Outputs” Continuing from the previous example, Ana details subsequent instructions that compare values in memory and print results. She describes how the ALU checks for equivalence between two numbers, showing the flow of control through addition, comparison, and final output, reinforcing the importance of every step in understanding programming processes. 14:04-15:02 “The Turing Machine Concept” Ana references Alan Turing’s concept of the Turing machine, explaining how it demonstrates that any computable function can be created using a minimal set of primitive operations. She emphasizes the significance of this principle: any programming language can be broken down to these basic operations, thereby allowing for the construction of equivalent programs across different languages. 15:02-15:28 “Introduction to Python and its Primitives” Shifting focus to practical programming, Ana outlines the advantages of Python's higher-level primitives compared to Turing's basic operations. She conveys that Python allows for more accessible, efficient coding, encouraging students to engage with the language's features, which will be more straightforward than coding in lower-level languages. 15:28-16:12 “Primitives in Language Comparison” Ana draws an analogy between programming languages and natural languages, explaining that just as English has words, Python has numbers, characters, and operators. This comparison sets the stage for understanding how these basic units form more complex constructs in a programming language, similar to how words create sentences in English. 16:12-17:08 “Building Syntactically Valid Expressions” Using examples from both English and Python, Ana illustrates the importance of syntax in programming. She explains that just as certain combinations of words can be syntactically valid while lacking meaning, so can programming expressions. This highlights the necessity of correct syntax and meaningful combinations of elements within a programming context. 17:08-18:14 “Static Semantics and Meaning in Programming” Ana elaborates on the concept of static semantics, contrasting it with syntax. She explains that while some expressions may be syntactically correct, if they fail to convey meaning or produce valid outcomes (as seen in her example with operators), they result in errors. This understanding is critical for students as they learn to write code that is both syntactically correct and semantically meaningful in Python. 18:14-19:10 “Differences in Meaning: Programming vs. English” Ana discusses the fundamental differences in semantics between English and programming languages. While English phrases can possess multiple meanings and interpretations, programming languages enforce strict definitions, where instructions are executed precisely as written without ambiguity. She emphasizes that each program has a single, clear meaning, but this can lead to misunderstandings if the programmer's intent does not align with the actual functionality of the code. 19:10-20:08 “Understanding Semantic Errors” The instructor highlights various types of errors programmers might encounter, distinguishing between syntactic errors and more complex semantic errors. While more straightforward errors like spelling are typically easy to identify and correct, semantic issues can be elusive, leading to frustration when the code behaves unexpectedly or produces incorrect results. Ana prepares students for future discussions on semantic errors and how they can affect programming outcomes. 20:08-20:57 “Programming Environments: Shell vs. Code Editor” Ana introduces the practical aspect of programming by explaining the environments in which students will work. For the day, they will use the shell for immediate feedback and basic command executions, with a transition to a code editor in future sessions. She encourages students to install the programming environment and hints at the utility of the shell for quick checks and minor coding tasks, reinforcing its importance alongside a more comprehensive code editor.
@pandaxpanther8128
High School freshman here interested in a cs career, and this course so far seems amazing, fast paced but very helpful. Thank you very much for this free course.
@oakleyorbit
Already working as a SWE but coming back is really good for brushing up and sharpening your skills.
@clintonbell_0
African Student learning programming through this invaluable course.... Thank you MIT
@claudiouschinyengetere2690
"I almost gave up on CS because of a lecturer who teaches as if we already know programming. Thanks for the videos!" 😊🎉
@Nikhilsamadhiya0
Man, this is invaluable. I mean some universities don't even public their degree requirements and here mit giving all lectures and that's too on cs. We Love you MIT❤❤ From India🇮🇳
@weixuanpks6050
best course ever. took this few years ago to learn python
@mitocw